Domino Data Lab

JUNE 23, 2019

Skomoroch proposes that managing ML projects are challenging for organizations because shipping ML projects requires an experimental culture that fundamentally changes how many companies approach building and shipping software. There wasn’t much more information on the profile. Yet, this challenge is not insurmountable.

Management 110

Management Machine Learning Experimentation Metrics 110

AWS Big Data

JULY 3, 2023

Look – ahead bias – This is a common challenge in backtesting, which occurs when future information is inadvertently included in historical data used to test a trading strategy, leading to overly optimistic results. Sell 1 (PVH, PVH) 2022-09-06 18321.729571 55.15 Sell 2 (INVH, INVH) 2022-09-06 27419.797094 38.20

Snapshot 84

Snapshot Data Lake Testing Strategy 84

Domino Data Lab

FEBRUARY 4, 2019

We’ll unpack curiosity as a core attribute of effective data science, look at how that informs process for data science (in contrast to Agile, etc.), and dig into details about where science meets rhetoric in data science. Of course, some questions in business cannot be answered with historical data.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

Cloudera

JUNE 6, 2019

When a mix of batch, interactive, and data serving workloads are added to the mix, the problem becomes nearly intractable. While this approach provides isolation, it creates another significant challenge: duplication of data, metadata, and security policies, or ‘split-brain’ data lake. Conclusion and future work.

Metadata 86

Metadata Data Lake Strategy Optimization 86

Domino Data Lab

MAY 8, 2019

For example, common practices for collecting data to build training datasets tend to throw away valuable information along the way. The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Information can get quite distorted after being abstracted that many times.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Occam's Razor

SEPTEMBER 15, 2014

Let's get the information we need to get our CEOs, CMOs and CUXOs to unleash a tsunami of creativity and awesomeness when it comes to mobile. In this post we will look mobile sites first, both data collection and analysis, and then mobile applications. Media-Mix Modeling/Experimentation. Framing the Opportunity.

Metrics 141

Metrics Reporting Analytics Marketing 141

Occam's Razor

AUGUST 13, 2012

Ensure you don't have to wait for "Web Authors" (or HiPPOs) to give you the information you need. Do the work that will make your baby-boomer c-levels care about the site and data (assuming you discover the site's of value) and. Help you focus on providing analysis rather than simply puking data out.

Modeling 124

Modeling Metrics Data Quality Data-driven 124

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

Implicitly, there was a prior belief about some interesting causal mechanism or an underlying hypothesis motivating the collection of the data. As computing and storage have made data collection cheaper and easier, we now gather data without this underlying motivation.

Experimentation 61

Experimentation Testing Statistics Metrics 61

Occam's Razor

JANUARY 7, 2014

First… it is important to realize that big data's big imperative is driving big action. Second… well there is no second, it is all about the big action and getting a big impact on your bottom-line from your big investment in analytics processes, consulting, people and tools.

Data-driven 140

Data-driven Consulting Reporting Marketing 140

Occam's Razor

APRIL 29, 2013

It turns out that Marketers, especially Digital Marketers, make really silly mistakes when it comes to data. Small data. Here's why… Real-time data is very expensive. It is expensive from a systems/platforms/data processing/data reporting perspective. PPS: I've mentioned one exception in the past.

Marketing 166

Marketing Metrics Advertising Measurement 166

Occam's Razor

OCTOBER 12, 2017

The tiny downside of this is that our parents likely never had to invest as much in constant education, experimentation and self-driven investment in core skills. Years and years of practice with R or "Big Data." They are entertaining, engaging and deeply informative. Years of having used tool x.

Marketing 136

Marketing Analytics Machine Learning Strategy 136